THE HYDROGEN INFRASTRUCTURE TRANSITION (HIT) MODEL ---- CASE STUDY FOR URBAN BEIJING

摘要

We introduce the Hydrogen Infrastructure Transition (HIT) model and apply it to Beijing, China. The HIT model is a dynamic programming model that generates the spatial and temporal infrastructure buildup decisions that minimize the net present value of capital and operating costs, carbon taxes, and refueling travel time disbenefits over time. The HIT model incorporates regionally specific spatial data about road networks, traffic flows and hydrogen demand distribution to find optimal strategies for meeting an exogenously specified market penetration over time. Input assumptions can be varied to test the sensitivity of strategies to technological evolution, feedstock prices, carbon taxes, and market penetration rates. We consider 4 scenarios: base case, increasing natural gas prices, rapid technology improvement, and rapid market penetration. For each scenario, we show 1) the least-cost spatial and temporal decisions generated by the HIT model; 2) the optimal infrastructure layout; 3) levelized costs over time; 4) well-to-wheel carbon emissions over time. Our findings are as follows: 1) the starting infrastructure configuration for all the 4 scenarios during 2010 to 2014 (beginning of the planning horizon) is 30 onsite steam methane reformer (SMR) stations with 3 ton per day per station. These stations serve only hydrogen taxis and buses (assuming the government will first introduce hydrogen taxis and buses) and provide a basis to attract the private fuel cell vehicle purchase, which is assumed to start from year 2015. 2) Regional spatial features have a significant impact on cost. Using a spanning tree optimization algorithm, we find that the high vehicle density and ring road network in urban Beijing can be served by a compact pipeline network with a total length of several hundred kilometers. This is shorter than previously reported pipeline designs. 3) Faster market penetration could make a better business case because scale economies in production and delivery can be taken advantage of earlier. 4) Carbon policy would need to keep pace with market penetration to avoid high CO2 emissions from coal gasification plants without carbon capture technology. If demand increases rapidly, a higher carbon tax might be needed to drive the adoption of carbon capture technology. 5) Faster technology improvement lowers cost. 6) For each scenario, we examine the levelized cost over time for a 12% rate of return. For the base case, the pricing policy of $2.8/kg from 2010 through 2019, $1.8/kg from 2020 through 2059 and $1.1/kg from 2060 onward could achieve a 12% rate of return, ignoring the effect of price on demand.